181 lines
9.8 KiB
Python
181 lines
9.8 KiB
Python
# services/training/app/schemas/training.py
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"""
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Pydantic schemas for training service
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"""
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from pydantic import BaseModel, Field, validator
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from typing import Dict, List, Any, Optional
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from datetime import datetime
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from enum import Enum
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class TrainingStatus(str, Enum):
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"""Training job status enumeration"""
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PENDING = "pending"
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RUNNING = "running"
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COMPLETED = "completed"
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FAILED = "failed"
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CANCELLED = "cancelled"
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class TrainingJobRequest(BaseModel):
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"""Request schema for starting a training job"""
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products: Optional[List[str]] = Field(None, description="Specific products to train (if None, train all)")
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include_weather: bool = Field(True, description="Include weather data in training")
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include_traffic: bool = Field(True, description="Include traffic data in training")
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start_date: Optional[datetime] = Field(None, description="Start date for training data")
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end_date: Optional[datetime] = Field(None, description="End date for training data")
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min_data_points: int = Field(30, description="Minimum data points required per product")
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estimated_duration: Optional[int] = Field(None, description="Estimated duration in minutes")
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# Prophet-specific parameters
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seasonality_mode: str = Field("additive", description="Prophet seasonality mode")
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daily_seasonality: bool = Field(True, description="Enable daily seasonality")
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weekly_seasonality: bool = Field(True, description="Enable weekly seasonality")
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yearly_seasonality: bool = Field(True, description="Enable yearly seasonality")
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@validator('seasonality_mode')
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def validate_seasonality_mode(cls, v):
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if v not in ['additive', 'multiplicative']:
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raise ValueError('seasonality_mode must be additive or multiplicative')
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return v
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@validator('min_data_points')
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def validate_min_data_points(cls, v):
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if v < 7:
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raise ValueError('min_data_points must be at least 7')
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return v
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class SingleProductTrainingRequest(BaseModel):
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"""Request schema for training a single product"""
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include_weather: bool = Field(True, description="Include weather data in training")
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include_traffic: bool = Field(True, description="Include traffic data in training")
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start_date: Optional[datetime] = Field(None, description="Start date for training data")
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end_date: Optional[datetime] = Field(None, description="End date for training data")
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# Prophet-specific parameters
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seasonality_mode: str = Field("additive", description="Prophet seasonality mode")
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daily_seasonality: bool = Field(True, description="Enable daily seasonality")
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weekly_seasonality: bool = Field(True, description="Enable weekly seasonality")
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yearly_seasonality: bool = Field(True, description="Enable yearly seasonality")
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class TrainingJobResponse(BaseModel):
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"""Response schema for training job creation"""
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job_id: str = Field(..., description="Unique training job identifier")
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status: TrainingStatus = Field(..., description="Current job status")
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message: str = Field(..., description="Status message")
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tenant_id: str = Field(..., description="Tenant identifier")
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created_at: datetime = Field(..., description="Job creation timestamp")
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estimated_duration_minutes: int = Field(..., description="Estimated completion time in minutes")
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class TrainingStatusResponse(BaseModel):
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"""Response schema for training job status"""
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job_id: str = Field(..., description="Training job identifier")
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status: TrainingStatus = Field(..., description="Current job status")
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progress: int = Field(0, description="Progress percentage (0-100)")
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current_step: str = Field("", description="Current processing step")
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started_at: datetime = Field(..., description="Job start timestamp")
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completed_at: Optional[datetime] = Field(None, description="Job completion timestamp")
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results: Optional[Dict[str, Any]] = Field(None, description="Training results")
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error_message: Optional[str] = Field(None, description="Error message if failed")
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class ModelInfo(BaseModel):
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"""Schema for trained model information"""
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model_id: str = Field(..., description="Unique model identifier")
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model_path: str = Field(..., description="Path to stored model")
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model_type: str = Field("prophet", description="Type of ML model")
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training_samples: int = Field(..., description="Number of training samples")
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features: List[str] = Field(..., description="List of features used")
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hyperparameters: Dict[str, Any] = Field(..., description="Model hyperparameters")
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training_metrics: Dict[str, float] = Field(..., description="Training performance metrics")
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trained_at: datetime = Field(..., description="Training completion timestamp")
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data_period: Dict[str, str] = Field(..., description="Training data period")
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class ProductTrainingResult(BaseModel):
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"""Schema for individual product training result"""
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product_name: str = Field(..., description="Product name")
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status: str = Field(..., description="Training status for this product")
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model_info: Optional[ModelInfo] = Field(None, description="Model information if successful")
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data_points: int = Field(..., description="Number of data points used")
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error_message: Optional[str] = Field(None, description="Error message if failed")
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trained_at: datetime = Field(..., description="Training completion timestamp")
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class TrainingResultsResponse(BaseModel):
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"""Response schema for complete training results"""
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job_id: str = Field(..., description="Training job identifier")
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tenant_id: str = Field(..., description="Tenant identifier")
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status: TrainingStatus = Field(..., description="Overall job status")
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products_trained: int = Field(..., description="Number of products successfully trained")
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products_failed: int = Field(..., description="Number of products that failed training")
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total_products: int = Field(..., description="Total number of products processed")
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training_results: Dict[str, ProductTrainingResult] = Field(..., description="Per-product results")
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summary: Dict[str, Any] = Field(..., description="Training summary statistics")
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completed_at: datetime = Field(..., description="Job completion timestamp")
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class TrainingValidationResult(BaseModel):
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"""Schema for training data validation results"""
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is_valid: bool = Field(..., description="Whether the data is valid for training")
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issues: List[str] = Field(default_factory=list, description="List of data quality issues")
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recommendations: List[str] = Field(default_factory=list, description="Recommendations for improvement")
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estimated_time_minutes: int = Field(..., description="Estimated training time in minutes")
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products_analyzed: int = Field(..., description="Number of products analyzed")
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total_data_points: int = Field(..., description="Total data points available")
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class TrainingMetrics(BaseModel):
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"""Schema for training performance metrics"""
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mae: float = Field(..., description="Mean Absolute Error")
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mse: float = Field(..., description="Mean Squared Error")
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rmse: float = Field(..., description="Root Mean Squared Error")
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mape: float = Field(..., description="Mean Absolute Percentage Error")
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r2_score: float = Field(..., description="R-squared score")
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mean_actual: float = Field(..., description="Mean of actual values")
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mean_predicted: float = Field(..., description="Mean of predicted values")
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class ExternalDataConfig(BaseModel):
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"""Configuration for external data sources"""
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weather_enabled: bool = Field(True, description="Enable weather data")
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traffic_enabled: bool = Field(True, description="Enable traffic data")
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weather_features: List[str] = Field(
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default_factory=lambda: ["temperature", "precipitation", "humidity"],
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description="Weather features to include"
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)
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traffic_features: List[str] = Field(
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default_factory=lambda: ["traffic_volume"],
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description="Traffic features to include"
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)
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class TrainingJobConfig(BaseModel):
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"""Complete training job configuration"""
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external_data: ExternalDataConfig = Field(default_factory=ExternalDataConfig)
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prophet_params: Dict[str, Any] = Field(
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default_factory=lambda: {
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"seasonality_mode": "additive",
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"daily_seasonality": True,
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"weekly_seasonality": True,
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"yearly_seasonality": True
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},
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description="Prophet model parameters"
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)
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data_filters: Dict[str, Any] = Field(
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default_factory=dict,
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description="Data filtering parameters"
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)
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validation_params: Dict[str, Any] = Field(
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default_factory=lambda: {"min_data_points": 30},
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description="Data validation parameters"
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)
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class TrainedModelResponse(BaseModel):
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"""Response schema for trained model information"""
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model_id: str = Field(..., description="Unique model identifier")
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tenant_id: str = Field(..., description="Tenant identifier")
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product_name: str = Field(..., description="Product name")
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model_type: str = Field(..., description="Type of ML model")
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model_path: str = Field(..., description="Path to stored model")
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version: int = Field(..., description="Model version")
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training_samples: int = Field(..., description="Number of training samples")
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features: List[str] = Field(..., description="List of features used")
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hyperparameters: Dict[str, Any] = Field(..., description="Model hyperparameters")
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training_metrics: Dict[str, float] = Field(..., description="Training performance metrics")
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is_active: bool = Field(..., description="Whether model is active")
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created_at: datetime = Field(..., description="Model creation timestamp")
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data_period_start: Optional[datetime] = Field(None, description="Training data start date")
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data_period_end: Optional[datetime] = Field(None, description="Training data end date") |